import csv

import tweepy

# Twitter API credentials
_SCREAMING_SNAKE_CASE 		=      """"""
_SCREAMING_SNAKE_CASE 		=      """"""
_SCREAMING_SNAKE_CASE 		=      """"""
_SCREAMING_SNAKE_CASE 		=      """"""





def 			lowercase(	UpperCamelCase_       )		->	None:







 '''simple docstring'''
 # authorize twitter, initialize tweepy
 UpperCamelCase			  =      tweepy.OAuthHandler(UpperCamelCase_							,					UpperCamelCase_       )
 auth.set_access_token(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      tweepy.API(UpperCamelCase_       )

 # initialize a list to hold all the tweepy Tweets
 UpperCamelCase			  =      []

 # make initial request for most recent tweets (200 is the maximum allowed count)
 UpperCamelCase			  =      api.user_timeline(screen_name=UpperCamelCase_							,					count=200       )

 # save most recent tweets
 alltweets.extend(UpperCamelCase_       )

 # save the id of the oldest tweet less one
 UpperCamelCase			  =      alltweets[-1].id - 1

 # keep grabbing tweets until there are no tweets left to grab
 while len(UpperCamelCase_       ) > 0:
  print(f"""getting tweets before {oldest}"""       )

  # all subsequent requests use the max_id param to prevent duplicates
  UpperCamelCase			  =      api.user_timeline(
      screen_name=UpperCamelCase_							,					count=200							,					max_id=UpperCamelCase_       )

  # save most recent tweets
  alltweets.extend(UpperCamelCase_       )

  # update the id of the oldest tweet less one
  UpperCamelCase			  =      alltweets[-1].id - 1

  print(f"""...{len(UpperCamelCase_       )} tweets downloaded so far"""       )

 # transform the tweepy tweets into a 2D array that will populate the csv
 UpperCamelCase			  =      [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]

 # write the csv
 with open(f"""new_{screen_name}_tweets.csv"""							,					"""w"""       ) as f:
  UpperCamelCase			  =      csv.writer(UpperCamelCase_       )
  writer.writerow(["""id""", """created_at""", """text"""]       )
  writer.writerows(UpperCamelCase_       )


if __name__ == "__main__":
     # pass in the username of the account you want to download
     get_all_tweets("""FirePing32""")

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      3
    UpperCamelCase			  =      250

    UpperCamelCase			  =      ids_tensor((batch_size, length)   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.ones((batch_size, length)   ,   device=lowerCamelCase_   ,   dtype=torch.float				) / length
    return input_ids, scores







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10				),
            MaxTimeCriteria(max_time=0.1				),
        ]				)

    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxLengthCriteria(max_length=10				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxNewTokensCriteria(start_length=5   ,   max_new_tokens=5				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      StoppingCriteriaList([criteria]				)
    self.assertEqual(criteria_list.max_length   ,   10				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1   ,   initial_timestamp=time.time() - 0.2				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   10				)

    with self.assertWarns(lowerCamelCase_				):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   11				)

    UpperCamelCase			  =      validate_stopping_criteria(StoppingCriteriaList()   ,   11				)

    self.assertEqual(len(lowerCamelCase_				)   ,   1				)
